The corresponding procedure will no longer be truly online and instead involve storing all the data points, but is still faster than the brute force method.
This discussion is restricted to the case of the square loss, though it can be extended to any convex loss.
Mini-batch techniques are used with repeated passing over the training data to obtain optimized out-of-core versions of machine learning algorithms, for e.g. When combined with backpropagation, this is currently the de facto training method for training artificial neural networks.
The simple example of linear least squares is used to explain a variety of ideas in online learning.
is a space of functions called a hypothesis space, so that some notion of total loss is minimised.
A simplified version of the KRLMS algorithm is also presented by applying only partial updating information to train the algorithm at each iteration, which reduces the computational complexity.
In this paper, we propose a novel type of kernel least mean square algorithm with regularized structural risk for online learning.
In order to curb the continuous growing of kernel functions, a new dictionary selection method based on the cumulative coherence measure is applied to perform the sparsification procedure, which can obtain a dictionary with diagonally dominant Gram matrix under certain conditions.
The ideas are general enough to be applied to other settings, for e.g. In the setting of supervised learning with the square loss function, the intent is to minimize the empirical loss, In practice, one can perform multiple stochastic gradient passes (also called cycles or epochs) over the data.
The algorithm thus obtained is called incremental gradient method and corresponds to an iteration -th step.